Instructions to use learn3r/bart_memsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use learn3r/bart_memsum with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("learn3r/bart_memsum") model = AutoModelForSeq2SeqLM.from_pretrained("learn3r/bart_memsum") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 80cfbf5b0674c7a7687936c86255422032bc2da19b117b7211054237cb858559
- Size of remote file:
- 4.22 kB
- SHA256:
- aafcc4509e5cb8ce373d8a9d6a7d233cb723a03e1d6f988daeaf9294a2ad9cc3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.